Browsing by Author "Pretis, Felix"
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Item Analysing differences between scenarios(International Journal of Forecasting, 2023) Hendry, David F.; Pretis, FelixComparisons between alternative scenarios are used in many disciplines, from macroeconomics through epidemiology to climate science, to help with planning future responses. Differences between scenario paths are often interpreted as signifying likely differences between outcomes that would materialise in reality. However, even when using correctly specified statistical models of the in-sample data generation process, additional conditions are needed to sustain inferences about differences between scenario paths. We consider two questions in scenario analyses: First, does testing the difference between scenarios yield additional insight beyond simple tests conducted on the model estimated in-sample? Second, when does the estimated scenario difference yield unbiased estimates of the true difference in outcomes? Answering the first question, we show that the calculation of uncertainties around scenario differences raises difficult issues, since the underlying in-sample distributions are identical for both ‘potential’ outcomes when the reported paths are deterministic functions. Under these circumstances, a scenario comparison adds little beyond testing for the significance of the perturbed variable in the estimated model. Resolving the second question, when models include multiple covariates, inferences about scenario differences depend on the relationships between the conditioning variables, especially their invariance to the interventions being implemented. Tests for invariance based on the automatic detection of structural breaks can help identify the in-sample invariance of models to evaluate likely constancy in projected scenarios. Applications of scenario analyses to impacts on the UK’s wage share from unemployment and agricultural growth from climate change illustrate the concepts.Item Large weather and conflict effects on internal displacement in Somalia with little evidence of feedback onto conflict(Global Environmental Change, 2023) Thalheimer, Lisa; Schwarz, Moritz P.; Pretis, FelixExtreme weather and conflict may drive forced displacement. However, their individual contribution to displacement is not fully understood due to challenges around isolating individual channels of causality. Here, we use novel disaggregated data on internal displacement in all of Somalia’s subregions from 2016 to 2018 broken down by reported reason of displacement and combine it with weather and conflict data. This allows us to isolate the effects of extreme weather and conflict on forced displacement, as well as the effects of displacement on conflict itself. We find large non-linear effects of weather on displacement where an increase in temperature anomalies from 1 °C to 2 °C (to approx. 1.5 standard deviations, SD) leads to a tenfold increase in displaced people, and a reduction in precipitation from 50 mm to 0 mm (approx. 1.5SD) leads to around a fourfold increase in displacement. We find significant effects of conflict events on displacement (which are masked when the data is aggregated) with a 1.5 standard deviation increase in conflict events increasing displacement 50-fold. We further show that displacement itself has little detectable effect on the occurrence of conflict events.